The prediction of cardiovascular disease based on trace element contents in hair and a classifier of boosting decision stumps

Relationship between Copper, Zinc, and Copper-to-Zinc Ratio in Hair and Severity of Coronary Artery Disease according to the SYNTAX Score

Coronary artery disease (CAD) continues to be a foremost contributor to global mortality, and the quest for modifiable risk factors could improve prophylactic strategies. Recent studies suggest a significant role of zinc (Zn) and copper (Cu) deficiency in atheromatous plaque formation. Furthermore, hair was previously described as a valuable source of information on elemental burden during the 6-8 week period before sampling. The aim of this study was to investigate the possibility of correlation between the extent of CAD evaluated with the SYNergy Between PCI With TAXUS and the Cardiac Surgery (SYNTAX) score with Cu and Zn content in hair samples, as well as with the Cu/Zn ratio in a cohort of 130 patients. Our findings describe a statistically significant inverse correlation between Cu content and the Cu/Zn ratio in hair samples and the extent of CAD. In contrast, no significant correlation was found between Zn content and the extent of CAD. Considering the scarcity of existing data on the subject, the analysis of hair samples could yield a novel insight into elemental deficiencies and their potential influence on CAD extent.

Investigation on the Association of Copper and Copper-to-Zinc-Ratio in Hair with Acute Coronary Syndrome Occurrence and Its Risk Factors

The prevalence of coronary artery disease (CAD) increases every year; however, the impact of microelements on its underlying cause—atherosclerosis—is still unclear. Copper plays numerous regulatory roles in cardiovascular health and was suggested to influence the classic risk factors for CAD. The copper-to-zinc-ratio (Cu/Zn-ratio) reflects systemic oxidative stress–one of the factors in the complex pathogenesis of atherosclerosis. Hair incorporates metal ions during its growth; thus, it reflects the metal exposure that occurred over the last 4–8 weeks. The aim of the presented study was to verify the association between Cu and Cu/Zn-ratio content and the occurrence of acute coronary syndrome (ACS) in the hair of 133 patients who underwent coronary angiography due to suspected ACS. Additionally, association between Cu and Cu/Zn-ratio and selected risk factors for CAD was analyzed. Neither Cu nor Cu/Zn-ratio levels were associated with the occurrence of ACS, regardless of its type (UA/NSTEMI/STEMI). We did not find a significant association between Cu content in hair and risk factors for CAD. The Cu/Zn-ratio in hair was significantly correlated only with body mass index. The relationship of Cu content and Cu/Zn-ratio in hair with CAD, its risk factors and ACS appears to be complex and requires further well-designed research.

Heavy metals controlling cardiovascular diseases risk factors in myocardial infarction patients in critically environmentally heavy metal-polluted steel industrial town Mandi-Gobindgarh (India)

Heavy metals (HMs) have a very significant clinical role in the pathogenesis, progression and management of cardiovascular diseases (CVDs). The prevalence of CVDs was reported to be higher in critically environmentally HM-polluted (EHMP) steel industrial town Mandi-Gobindgarh (India) for the last more than a decade. To ascertain the role of HMs in the onset of CVDs, the present study was chosen to investigate HMs content in myocardial infarction (MI) patients from EHMP steel industrial town Mandi-Gobindgarh. Total of 110 MI patients along with number- and age-matched healthy volunteers were recruited in the present investigation. The CVDs risk factors estimated in MI patients were overweight (higher body mass index), hypertension (higher systolic and diastolic blood pressures), dyslipidaemia (higher serum cholesterol, triglycerides and lower HDL cholesterol), inflammation (higher-serum C reactive protein and aldosterone) and elevated oxidative stress (higher urinary 8-hydroxydeoxyguanosine). An imbalance of serum electrolyte concentrations including Na (hypernatremia), Ca (hypercalcaemia) and K (hypokalaemia) was also observed in MI patients in which CVDs risk factors were found to correlate positively with serum Na and Ca and negatively with serum K, respectively. Hair HM analysis was used as a bio-indicator for monitoring body HM status from past environmental HM exposure in which CVDs risk factors were observed to correlate positively with higher hair concentrations of Zn, Fe, Mo, Pb, As, Ca and Na and negatively with lower hair concentrations of Cu, Mg, Mn and K in MI patients, respectively. Thus, higher hair concentrations of Zn and Pb indicate their higher environmental exposure and possible cause of higher CVDs risk factors in MI patients from Mandi-Gobindgarh.

A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review

Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods. The proposed study reviews the three most recent paradigms for CVD risk assessment, namely multiclass, multi-label, and ensemble-based methods in (i) office-based and (ii) stress-test laboratories. Methods: A total of 265 CVD-based studies were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model. Due to its popularity and recent development, the study analyzed the above three paradigms using machine learning (ML) frameworks. We review comprehensively these three methods using attributes, such as architecture, applications, pro-and-cons, scientific validation, clinical evaluation, and AI risk-of-bias (RoB) in the CVD framework. These ML techniques were then extended under mobile and cloud-based infrastructure. Findings: Most popular biomarkers used were office-based, laboratory-based, image-based phenotypes, and medication usage. Surrogate carotid scanning for coronary artery risk prediction had shown promising results. Ground truth (GT) selection for AI-based training along with scientific and clinical validation is very important for CVD stratification to avoid RoB. It was observed that the most popular classification paradigm is multiclass followed by the ensemble, and multi-label. The use of deep learning techniques in CVD risk stratification is in a very early stage of development. Mobile and cloud-based AI technologies are more likely to be the future. Conclusions: AI-based methods for CVD risk assessment are most promising and successful. Choice of GT is most vital in AI-based models to prevent the RoB. The amalgamation of image-based strategies with conventional risk factors provides the highest stability when using the three CVD paradigms in non-cloud and cloud-based frameworks.

Associations between dietary antioxidant intakes and cardiovascular disease

Cardiovascular disease (CVD), as the leading cause of death worldwide, is the collective term named for disorders afflicting the blood vessels and heart. Inflammation and enhanced oxidative stress have been shown as fundamental risk factors in the onset and progression of CVD. Chronic inflammatory conditions attenuate blood levels of antioxidants because of the continuous generation of elevated levels of reactive oxygen species (ROS). A sufficient intake of antioxidants is also suggested to beneficially interfere with CVD by quenching ROS. Antioxidant vitamins and minerals, such as vitamins A, E, and C, and zinc may slow the development and progression of CVD. This study aimed at investigating the association between daily consumption of dietary vitamins A, E, and C, and zinc and the incidence of CVD. Eligible adults (n = 5102) aged ≥ 30 years, were selected from the participants of the Tehran lipid and glucose study with an average follow-up of 5.3 years. Dietary intake was assessed using a valid and reliable semi-quantitative food frequency questionnaire. Anthropometrics and biochemical variables were evaluated at baseline and follow-up examinations. Multivariable Cox proportional hazard regression models were used to estimate the development of CVD associated with total intakes of vitamins A, E, and C, and zinc. This study was conducted on 2253 men and 2849 women aged 47.0 ± 11.6 and 45.6 ± 10.5 years, respectively. The main sources of dietary vitamins A, E, and C and zinc were fruits, vegetables, and legumes. Risk of CVD decreased from quartile 1 to quartile 4 for vitamin E intake (HR 1.00, 0.91, 0.77, and 0.57; P_trend = 0.03). The association between the risk of CVD and quartiles of vitamins A, and C and zinc intake was not statistically significant. Our study suggests an inverse association between vitamin E intake and the risk of CVD, emphasizing the potential protective role of fruit and vegetable in the prevention of CVD.

Association of Zinc and Copper Status with Cardiovascular Diseases and their Assessment Methods: A Review Study

Cardiovascular disease (CVD) is the leading cause of mortality, morbidity, and financial losses and has a high prevalence across the world. Several studies have investigated the association between various CVD types with zinc and copper status as the essential minerals for the human body, proposing contradictory and similar results. This narrative review aimed to survey the correlations between zinc and copper status in the human body and some risk factors of CVD, as well as the assessment methods of zinc and copper status in the human body. According to the reviewed articles, zinc and copper deficiency may increase the risk of coronary heart disease, valvular regurgitation, and myocardial lesions, cardiac hypertrophy. Furthermore, it could lead to the expanded mitochondrial compartments of the heart, acute and chronic heart failure, and elevation of inflammation markers, such as interleukin-1 (IL-1) and IL-6. Two methods are primarily used for the assessment of zinc and copper in the human body, including the direct method (measurement of their concentrations) and indirect method (determining the activity of zinc- and copper-containing enzymes). Both these methods are considered reliable for the assessment of the zinc and copper levels in healthy individuals. Serum or plasma levels of these elements are also commonly used for the assessment of the correlation between zinc and copper status and CVD. But, which one is a more accurate indicator in relation to CVD is not yet clear; therefore, further studies are required in this field.

Artificial Intelligence in Nutrients Science Research: A Review

Artificial intelligence (AI) as a branch of computer science, the purpose of which is to imitate thought processes, learning abilities and knowledge management, finds more and more applications in experimental and clinical medicine. In recent decades, there has been an expansion of AI applications in biomedical sciences. The possibilities of artificial intelligence in the field of medical diagnostics, risk prediction and support of therapeutic techniques are growing rapidly. The aim of the article is to analyze the current use of AI in nutrients science research. The literature review was conducted in PubMed. A total of 399 records published between 1987 and 2020 were obtained, of which, after analyzing the titles and abstracts, 261 were rejected. In the next stages, the remaining records were analyzed using the full-text versions and, finally, 55 papers were selected. These papers were divided into three areas: AI in biomedical nutrients research (20 studies), AI in clinical nutrients research (22 studies) and AI in nutritional epidemiology (13 studies). It was found that the artificial neural network (ANN) methodology was dominant in the group of research on food composition study and production of nutrients. However, machine learning (ML) algorithms were widely used in studies on the influence of nutrients on the functioning of the human body in health and disease and in studies on the gut microbiota. Deep learning (DL) algorithms prevailed in a group of research works on clinical nutrients intake. The development of dietary systems using AI technology may lead to the creation of a global network that will be able to both actively support and monitor the personalized supply of nutrients.

The Association between Mineral and Trace Element Concentrations in Hair and the 10-Year Risk of Atherosclerotic Cardiovascular Disease in Healthy Community-Dwelling Elderly Individuals

This cross-sectional analysis included 137 Korean subjects aged 60⁻79 years. All subjects underwent anthropometric measurements and laboratory tests. Scalp hair samples were obtained from each individual, the concentrations of 36 minerals and trace elements were analyzed, and 16 ratios of elements were calculated. ASCVD risk was estimated using pooled cohort ASCVD risk assessment equations for 10-year risk profiles. The 137 subjects were divided into three risk groups: low (<5%, n = 28), intermediate (5% to <7.5%, n = 21), and high (≥7.5%, n = 88) risk groups. After adjusting for obesity (BMI ≥ 25 kg/m²) and sex, Na concentration (mg%) in hair was significantly lower in the low-risk (13.91 ± 7.02) than in the intermediate-risk (47.18 ± 8.08) and high-risk (36.76 ± 3.95) groups (p for trend = 0.024). The concentration of K (mg%) in hair was also positively associated with the severity of ASCVD risk (10.50 ± 8.37, 23.62 ± 9.63, 33.31 ± 4.71, respectively; p for trend = 0.017), but their differences were not statistically significant (p = 0.059). By contrast, the levels of Co, U, and Hg, and the Ca/P and Ca/Mg ratios, were negatively correlated with the severity of ASCVD risk (p for trend < 0.05). Mean Na concentration in hair was significantly lower in the low-risk than in the other risk groups. By contrast, Co, U, and Hg concentrations showed significant negative associations with risk severity. Further studies are needed to assess whether dietary modification for trace elements could lower the risk of ASCVD.

Environmental Metals and Cardiovascular Disease in Adults: A Systematic Review Beyond Lead and Cadmium

Published systematic reviews concluded that there is moderate to strong evidence to infer a potential role of lead and cadmium, widespread environmental metals, as cardiovascular risk factors. For other non-essential metals, the evidence has not been appraised systematically. Our objective was to systematically review epidemiologic studies on the association between cardiovascular disease in adults and the environmental metals antimony, barium, chromium, nickel, tungsten, uranium, and vanadium. We identified a total of 4 articles on antimony, 1 on barium, 5 on chromium, 1 on nickel, 4 on tungsten, 1 on uranium, and 0 on vanadium. We concluded that the current evidence is not sufficient to inform on the cardiovascular role of these metals because of the small number of studies. Few experimental studies have also evaluated the role of these metals in cardiovascular outcomes. Additional epidemiologic and experimental studies, including prospective cohort studies, are needed to understand the role of metals, including exposure to metal mixtures, in cardiovascular disease development.

Prospective Study of Dietary Zinc Intake and Risk of Cardiovascular Disease in Women

Several animal and human studies have shown that zinc is associated with cellular damage and cardiac dysfunction. This study aims to investigate dietary zinc and the zinc-iron ratio, as predictors of incident cardiovascular disease (CVD) in a large longitudinal study of mid-age Australian women (aged 50-61 years). Data was self-reported and validated food frequency questionnaires were used to assess dietary intake. Energy-adjusted zinc was ranked using quintiles and predictors of incident CVD were examined using stepwise logistic regression. After six years of follow-up, 320 incident CVD cases were established. A positive association between dietary zinc intake, zinc-iron ratio and risk of CVD was observed even after adjusting for potential dietary and non-dietary confounders. Compared to those with the lowest quintile of zinc, those in the highest quintile (Odds Ratio (OR) = 1.67, 95% Confidence Interval (CI) = 1.08-2.62) and zinc-iron ratio (OR = 1.72, 95% CI = 1.05-2.81) had almost twice the odds of developing CVD (p trend = 0.007). This study shows that high dietary zinc intake and zinc-iron ratio is associated with a greater incidence of CVD in women. Further studies are required detailing the source of zinc and iron in diet and their precise roles when compared to other essential nutrients.

The inflammatory responses in Cu-mediated elemental imbalance is associated with mitochondrial fission and intrinsic apoptosis in Gallus gallus heart

Copper (Cu) is an essential trace element for organism of function properly. Overexposure to Cu causes chronic cardiac impairment. The aim of this study was to investigate the change of 28-trace element, inflammatory response, the possible mitochondrial dynamics and apoptosis under Cu exposure in the heart of chickens. Cupric sulfate (CuSO₄) (300 mg/kg) was administered in a basal diet to male Hy-line chickens (one-day-old) for 90 days. Results showed the concentrations of Cu in the Cu group were increased by 57.8%, 27.57% and 57.2% at 30, 60 and 90 days, respectively. The Cu supplement caused trace elements imbalance, including reduced concentrations of B, Al, Ni, Ba, Pb and increased Li, Na, Mg, Si, K, Ca, V, Mn, Fe, Co, Zn, As, Mo in the heart of chickens. Exposure to Cu induced the TUNEL positive nuclei, histopathological alterations and ultrastructural apoptotic features. Moreover, Cu exposure activated the NF-κB-mediated pro-inflammatory cytokines, decreased the mRNA levels of opa1, mfn1, mfn2, Bcl-2, increased the mRNA levels of drp1, Bax, caspase-3, caspase-9, P53, while not altered Fas and caspase-8 compared with the control group. Similarly, western blot results showed the same trend of mRNA. Correlation analysis indicated that mitochondrial fission and intrinsic apoptosis might function synergistic. Moreover, mitochondrial network seem to function as cytosolic sensors for the induction of NF-κB mediated inflammatory responses. In summary, we speculated that Cu-induced redistribution of trace elements contributed to inflammatory response and disrupted the mitochondrial network via fission and intrinsic apoptosis in the heart of chickens.

Computational model for vitamin D deficiency using hair mineral analysis

Vitamin D deficiency is prevalent in the Arabian Gulf region, especially among women. Recent studies show that the vitamin D deficiency is associated with a mineral status of a patient. Therefore, it is important to assess the mineral status of the patient to reveal the hidden mineral imbalance associated with vitamin D deficiency. A well-known test such as the red blood cells is fairly expensive, invasive, and less informative. On the other hand, a hair mineral analysis can be considered an accurate, excellent, highly informative tool to measure mineral imbalance associated with vitamin D deficiency. In this study, 118 apparently healthy Kuwaiti women were assessed for their mineral levels and vitamin D status by a hair mineral analysis (HMA). This information was used to build a computerized model that would predict vitamin D deficiency based on its association with the levels and ratios of minerals. The first phase of the proposed model introduces a novel hybrid optimization algorithm, which can be considered as an improvement of Bat Algorithm (BA) to select the most discriminative features. The improvement includes using the mutation process of Genetic Algorithm (GA) to update the positions of bats with the aim of speeding up convergence; thus, making the algorithm more feasible for wider ranges of real-world applications. Due to the imbalanced class distribution in our dataset, in the second phase, different sampling methods such as Random Under-Sampling, Random Over-Sampling, and Synthetic Minority Oversampling Technique are used to solve the problem of imbalanced datasets. In the third phase, an AdaBoost ensemble classifier is used to predicting the vitamin D deficiency. The results showed that the proposed model achieved good results to detect the deficiency in vitamin D.

Environmental biomonitoring of essential and toxic elements in human scalp hair using accelerated microwave-assisted sample digestion and inductively coupled plasma optical emission spectroscopy

Human scalp hair samples were collected and used to assess exposure to toxic elements and essential elements in the state of North Carolina, USA using accelerated microwave assisted acid digestion and inductively coupled plasma optical emission spectroscopy (ICP-OES). The figures-of-merit of the ICP-OES were appropriate for elemental analysis in scalp hair with detection limits as low as 0.0001 mg/L for Cd, good linearity (R² > 0.9978), and percent recoveries that ranged from 96 to 106% for laboratory-fortified-blanks and 88-112% for sample spike recovery study. The concentrations of essential elements in scalp hair were larger than those of toxic elements, with Ca having the highest average concentration (3080 μg/g, s = 14,500, n = 194). Some of the maximum concentrations observed for As (65 μg/g), Ni (331 μg/g), Cd (2.96 μg/g), and Cr (84.6 μg/g) in individual samples were concerning, however. Samples were statistically analyzed to determine the influence of race, gender, smoking habits, or age on the elemental concentrations in scalp hair. Higher concentrations of essential elements were observed in the scalp hair of Caucasians, females, and non-smokers, and the differences were often significant at a 90% confidence level. Several pairs of essential elements, for example Ca-K, Ca-Mg, and Ca-Zn, were strongly correlated in Caucasian hair but uncorrelated in African-American hair. Similarly, essential elements were strongly correlated in female hair but weakly correlated in male hair. Toxic element pairs (As-Cd, As-Se, Pb-As, and Se-Cd) were strongly correlated in the hair of smokers but uncorrelated in that of non-smokers, suggesting that cigarette smoke is a common source of toxic elements in humans.

Gender as a key factor in trace metal and metalloid content of human scalp hair. A multi-site study

This multi-site study discusses the content of metals and metalloids (MM) in scalp hair of children, living in different environmental contexts, with the purpose of verifying if hair level of some MM is distinctively gender-specific. A total of 943 hair samples (537 females and 406 males) from adolescents were analyzed for their content of Al, As, Ba, Cd, Co, Cr, Cu, Li, Mn, Mo, Ni, Pb, Rb, Sb, Se, Sr, U, V and Zn. Elemental quantification was performed by ICP-MS. The obtained data identified different metal distributions in adolescent girls which exhibited significantly higher hair concentrations of some trace metals, especially Sr, Zn and Ni, than boys. On the base of the median value, hair of female donors contained 3.8 times more Sr (6.6μg/g) than males (1.7μg/g). Highest concentrations of Zn in females were observed in samples from the mining area of Sardinia (587μg/g). Nickel showed significant differences resulting 2.5-fold higher in female hair. Regardless of the residence site, these elements resulted always significantly different (at p<0.01) between female and male indicating that gender is a confounding factor that has to be more extensively considered for a correct interpretation of metal profiles in human hair.

Essential and Toxic Metals in Oral Fluid-a Potential Role in the Diagnosis of Periodontal Diseases

Recently, many studies have investigated the relationship between the level of metals in the body and various diseases. The objective of this study was to examine any possible influence of periodontal disease upon the concentration of metals in oral fluid and blood and to explore the usability of applying cluster analysis coupled with the analysis of selected elements in oral fluid, calcium (Ca), copper (Cu), iron (Fe), magnesium (Mg), manganese (Mn), zinc (Zn), cadmium (Cd) and lead (Pb), for effectively distinguishing people affected by periodontitis from healthy individuals. The quantification of eight metals in oral fluid and blood samples was performed by two inductively coupled plasma techniques-inductively coupled plasma mass spectrometry (ICP-MS) and inductively coupled plasma optical emission spectrometry (ICP-OES). Most of the examined elements were detected at elevated concentration in the oral fluid of periodontal patients. However, the differences were statistically significant in the case of three metals: Cu, Mg and Mn (p < 0.05). Approximately, fivefold increase in the concentration of Cu, threefold-elevated levels of Mn and a twofold increase in the concentration of Mg were found in the oral fluid of the periodontal patients compared to the controls. Cluster analysis confirmed the statistical significance of the differences in the level of metals in the oral fluid between the two groups in most cases, plus enabled the correct classification of the subjects into patients and controls. The relationship between concentrations of metals and periodontal disease may in the future serve to prevent the development of such disease.

Deficient zinc levels and myocardial infarction : association between deficient zinc levels and myocardial infarction: a meta-analysis

The purpose of this study is to clarify the association between Zn levels and myocardial infarction (MI) using a meta-analysis approach. We searched articles in the PubMed, OVID, and ScienceDirect published as of November 2014. Thirteen eligible articles with 2886 subjects from 41 case-control studies were identified. Overall, pooled analysis indicated that subjects with MI had lower Zn levels than healthy controls (standardized mean difference (SMD) = -1.848, 95 % confidence interval (CI) = [-2.365, -1.331]). Further subgroup analysis found that subjects with MI had lower Zn levels than healthy controls in serum (SMD = -1.764, 95 % CI = [-2.417, -1.112]) and hair (SMD = -3.326, 95 % CI = [-4.616, -2.036]), but not in toenail (SMD = -0.396, 95 % CI = [-1.114, 0.322]). The subgroup analysis stratified by type of Zn measurement found a similar pattern in inductively coupled plasma-atomic absorption spectrometry (ICP-AAS) (SMD = -2.442, 95 % CI = [-3.092, -1.753]), but not in neutron activation analysis (NAA) (SMD = -0.449, 95 % CI = [-1.127, 0.230]). Lower Zn levels in MI patients were found both in male (SMD = -3.350, 95 % CI = [-4.531, -2.169]) and female (SMD = -2.681, 95 % CI = [-3.440, -1.922]). And the difference of Zn levels according to MI in Asia (SMD = -2.555, 95 % CI = [-3.267, -1.844]) was greater to that among the population in Europe (SMD = -0.745, 95 % CI = [-1.386, -0.104]), but no difference was found in Oceania (SMD = -0.255, 95 % CI = [-0.600, 0.089]). In conclusion, this meta-analysis indicates that there is a significant association between Zn deficiency and MI. We suggest that a community-based, long-term observation in a cohort design should be performed to obtain better understanding of causal relationships between Zn and MI, through measuring hair Zn at baseline to investigate whether the highest zinc category versus lowest was associated with MI risk.

Classification models based on the level of metals in hair and nails of laryngeal cancer patients: diagnosis support or rather speculation?

Several larynx cancer prediction models were built and each was weighted according to their performance.

The diagnostics of diabetes mellitus based on ensemble modeling and hair/urine element level analysis

The aim of the present work focuses on exploring the feasibility of analyzing the relationship between diabetes mellitus and several element levels in hair/urine specimens by chemometrics. A dataset involving 211 specimens and eight element concentrations was used. The control group was divided into three age subsets in order to analyze the influence of age. It was found that the most obvious difference was the effect of age on the level of zinc and iron. The decline of iron concentration with age in hair was exactly consistent with the opposite trend in urine. Principal component analysis (PCA) was used as a tool for a preliminary evaluation of the data. Both ensemble and single support vector machine (SVM) algorithms were used as the classification tools. On average, the accuracy, sensitivity and specificity of ensemble SVM models were 99%, 100%, 99% and 97%, 89%, 99% for hair and urine samples, respectively. The findings indicate that hair samples are superior to urine samples. Even so, it can provide more valuable information for prevention, diagnostics, treatment and research of diabetes by simultaneously analyzing the hair and urine samples.

Hair minerals and metabolic health in Belgian elementary school girls

Literature has repeatedly shown a relationship between hair minerals and metabolic health, although studies in children are currently lacking. This study aims to investigate hair levels of calcium (Ca), copper (Cu), magnesium (Mg), iron (Fe), phosphorus (P), and zinc (Zn) and their association with (1) overweight/obesity and (2) metabolic health in Flemish elementary school girls between 5 and 10 years old. Two hundred eighteen girls participated in this study as part of the baseline ChiBS project. Children were subjected to physical examinations, blood and hair sampling. Hair minerals were quantitatively determined via inductively coupled plasma-mass spectrometry after microwave-assisted acid digestion. Body mass index (BMI) and body fat percentage (BF%) were studied as anthropometric parameters, and a metabolic score (including systolic and diastolic blood pressure, insulin resistance and non-high-density lipoprotein (non-HDL) cholesterol as parameters) was calculated, with higher scores indicating a more unhealthy metabolic profile. Hair Ca, Ca/Mg, and Ca/P positively correlated with the anthropometric parameters. An inverse correlation was observed between Ca, Mg, and Ca/P in hair and the metabolic score. Inverse correlations were also observed for individual metabolic parameters (i.e., diastolic blood pressure, homeostasis model assessment for insulin resistance, non-HDL cholesterol). In particular, girls with a total number of three or more metabolic parameters above the age-specific 75th percentile showed significantly reduced hair Ca, Mg, and Ca/P concentrations. This study showed reduced hair mineral concentrations in young girls with a more unhealthy metabolic profile. Positive associations were observed between some minerals and BMI and BF%.

Hair analysis in health assessment

BACKGROUND

Hair analysis is used for estimation of the nutritional status of individuals. In the present work, a systematic review on the relation between the mineral composition of hair and the physical or mental disorders is discussed. Detailed information of examined populations, methods of sample preparations and analytical techniques are presented.

METHODS

A systematic literature search in four electronic databases Scopus, PubMed, Web of Science and Medline (from 1997 to 2012/01/31) for English language articles was performed. In addition, a reference list and manual search was undertaken.

RESULTS

The following number of studies was included: 66. Most of the authors reported that there exists a correlation between deficiency or excess of some elements in hair and occurrence of some diseases, such as: autism, cancer, hypertension, myocardial infarction, kidney disease and diabetes mellitus. However, not all results were consistent.

CONCLUSIONS

Most of the authors concluded that the profile of hair mineral imbalance might be useful as a diagnostic tool for the early diagnosis of many diseases. However, it seems that there is a need to standardize sample preparation procedures, in particular washing and mineralization methods.

Prediction of type-2 diabetes based on several element levels in blood and chemometrics

The present study was designed to evaluate the levels of eight elements including lithium, zinc, chromium, copper, iron, manganese, nickel and vanadium in whole blood of type-2 diabetes patients, to compare them with age-matched healthy controls and to investigate the feasibility of combining them with an ensemble model for diagnosing purpose. A dataset involving 158 samples, among which 105 were taken from healthy adults and the remaining 53 from patients with type-2 diabetes, was collected. All samples were split into the training set and the test set with the equal size. Based on a simple variable selection, two elements, i.e., chromium and iron, are also picked out as the most important elements. Three kinds of algorithms, i.e., fisher linear discriminate analysis (FLDA), support vector machine (SVM) and decision tree (DT), were used for constructing member models. The best ensemble classifiers constructed on the training set were validated on the independent test set, and the prediction results were compared with those from clinical diagnostics on the same subjects. The results reveal that almost all ensemble classifiers exhibit similar performance, implying that these elements coupled with an appropriate ensemble classifier can serve as a valuable tool of diagnosing diabetes type-2.

Screening of prostate cancer by analyzing trace elements in hair and chemometrics

Prostate cancer is the most common non-cutaneous malignancy and second leading cause of cancer mortality in men. The principle goal of this study was explore the feasibility of applying boosting coupled with trace element analysis of hair, for accurately distinguishing prostate cancer from healthy person. A total of 113 subjects containing 55 healthy men and 58 prostate cancers were collected. Based on a special index of variable importance and a forward selection scheme, only nine elements (i.e., Zn, Cr, Mg, Ca, Al, P, Cd, Fe, and Mo) were picked out from 20 candidate elements for modeling the relationship. As a result, an ensemble classifier consisting of only eight decision stumps achieved an overall accuracy of 98.2%, a sensitivity of 100%, and a specificity of 96.4% on the independent test set while all subjects on the training set are classified correctly. It seems that integrating boosting and element analysis of hair can serve as a valuable tool of diagnosing prostate cancer in practice.

Classification models for detection of lung cancer based on nine element distribution of urine samples

The detection of lung cancer has a special value in the diagnosis of cancer diseases. Based on nine elemental concentrations (i.e., chromium, iron, manganese, aluminum, cadmium, copper, zinc, nickel, and selenium) in urine samples and an ensemble linear discriminant analysis (ELDA), a detection method for lung cancer has been developed. A dataset containing 30 healthy samples and 27 lung cancer samples is used for experiment. The whole dataset was first split into a training set with 29 samples and a test set with 28 samples. The prediction results from the ELDA classifier were compared with those from single Fisher's discriminate analysis (FDA). On the test set, the ELDA classifier achieved better performance, that is, a sensitivity of 100%, a specificity of 86.7%, and an overall accuracy of 92.9%, while the FDA classifier had a sensitivity of 92.3%, a specificity of 93.3%, and an overall accuracy of 92.9%. The superiority of ELDA to FDA is ascribed to the fact that ELDA can model more nonlinear relationships through the cooperation of several single models, suggesting that ensemble modeling is more advisable in such a task.

Modeling the relationship between cervical cancer mortality and trace elements based on genetic algorithm-partial least squares and support vector machines

The relationship between the mortality of cervical cancer and soil trace elements of 23 regions of China was investigated. A total of 25 elements (i.e., Na, K, Mg, Ca, Sr, Hg, Pb, B, Tm, Th, U, Sn, Hf, Bi, Ta, Te, Mo, Br, I, As, Cr, Cu, Fe, Zn, and Se) were considered. First, 23 samples were split into the training set with 12 samples and the test set with 11 samples. Then, a combination strategy called genetic algorithm-partial least squares (GA-PLS) was used to pick out five important elements. i.e., Br, Ta, Pb, Cr, and As. Afterwards, the classic partial least squares (PLS) model and least square support vector machine (LSSVM) model were developed and compared. The results revealed that the SVM model significantly outperforms the PLS model, indicating that the combination of GA-PLS and LSSVM can serve as a potential tool for predicting the mortality of cancer based on trace elements.

Analysis of the relationship between leukemia mortality and soil trace elements using chemometrics

The relationship between the mortality of leukemia and the contents of trace elements in the soils of 29 regions of China was investigated. A total of 27 elements were determined for each region. Considering that an efficient variable selection can be highly beneficial both to improve the predictive ability of the model and to greatly reduce its complexity, genetic algorithm-partial least squares was used to screen out 13 qualified elements. As a result, only 13 elements, i.e., As, Hg, Mn, Sr, Ba, Cu, Ti, Co, K, Ca, Rb, Zn, and Mg, were picked out and, a partial least squares model with three latent variables was obtained, whose prediction exhibited a correlative coefficient of 0.874 with actual mortality. Especially, it showed a high negative correlation between the content of soil As and the mortality of leukemia. Such a fact can be explained by the apoptotic effect of cancerous cells by trace-amount arsenic trioxide. Furthermore, according to whether the mortality was larger than two out of 100,000 (2 × 10⁻⁵), all the 29 regions were divided into 21 high-mortality regions and eight low-mortality regions and were assigned the label -1 or 1, respectively. Using the same 13 elements, a Fisher's discriminant analysis model was developed, which can successfully discriminate low- and high-mortality groups.